Investigation of methods for ensuring stability of control systems based on programmable logic controllers in conditions of dynamic changes

Abstract

Automated control systems, the key components of which are programmable logic controllers (PLCs), have become widespread in the modern world. However, maintaining the stability of such systems in the face of dynamic changes caused by external disturbances or changes in the characteristics of the control object remains a difficult task. This study is devoted to the study of methods for ensuring the stability of PLC-based control systems in rapidly changing operating conditions. To solve this problem, an analysis of existing PLC stabilization methods was carried out, including adaptive algorithms, signal filtering, and predictive control. The research was conducted using mathematical modeling of control processes, laboratory experiments on a physical prototype and testing in a virtual environment simulating dynamic changes. The effectiveness of stability assurance methods was assessed based on criteria such as response speed, control accuracy, and stable system behavior. Advanced techniques have been developed and tested, including adaptive regulators and predictive models such as neural networks. The results obtained demonstrate that the proposed approaches significantly increase the stability of PLC-based control systems, especially in the face of external interference. Experimental studies have shown that the system's adaptation time has been reduced by 25-30%, and the quality of management has improved compared to traditional approaches. The study confirms that the use of adaptive, predictive algorithms and new technologies can significantly improve the stability of PLC-based control systems. The developed methodology can be applied in various industries such as mechanical engineering, energy and robotics to ensure the reliability and durability of automated control systems.